Artificial intelligence-based process identification, extraction, and automation for robotic process automation

ABSTRACT

Artificial intelligence (AI)-based process identification, extraction, and automation for robotic process automation (RPA) is disclosed. Listeners may be deployed to user computing systems to collect data pertaining to user actions. The data collected by the listeners may then be sent to one or more servers and be stored in a database. This data may be analyzed by AI layers to recognize patterns of user behavioral processes therein. These recognized processes may then be distilled into respective RPA workflows and deployed to automate the processes.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication No. 62/915,340 filed Oct. 15, 2019. The subject matter ofthis earlier filed application is hereby incorporated by reference inits entirety.

FIELD

The present invention generally relates to robotic process automation(RPA), and more specifically, to artificial intelligence (AI)-basedprocess identification, extraction, and automation for RPA.

BACKGROUND

Businesses may or may not be aware of processes that could benefit fromRPA. Employees perform various tasks that are likely not directly knownby the employer, and these tasks may be repetitive or otherwisecandidates for being automated. Furthermore, businesses may have an ideaof what they would like to automate, but not know the best workflow(s)to achieve that automation. While a log of user actions could begenerated by a human reviewing a video recording, for example, this doesnot effectively capture precisely what a user is doing and is tooexpensive and time consuming to be practical and effective. Furthermore,the reviewer's account of what is occurring may not be accurate (e.g.,the reviewer may misidentify the application that a user is using at agiven time). Accordingly, an improved mechanism for identifyingbeneficial automations, improving planned or existing automations, orboth, may be beneficial.

SUMMARY

Certain embodiments of the present invention may provide solutions tothe problems and needs in the art that have not yet been fullyidentified, appreciated, or solved by current RPA techniques. Forexample, some embodiments of the present invention pertain to AI-basedprocess identification, extraction, and automation for RPA.

In an embodiment, a system includes a server and a plurality of usercomputing systems including respective listener applications. Thelistener applications are configured to generate logs including userinteractions with their respective user computing systems and send thelog data from the logs to the server. The server is configured to accesslog data collected from the listeners and run the log data through atleast one AI layer. The at least one AI layer is configured to processthe log data and identify a potential RPA process therein. The server isthen configured to automatically generate a workflow including theidentified RPA process.

In another embodiment, a computer program is embodied on a nontransitorycomputer-readable medium. The program is configured to cause at leastone processor to access log data collected from respective listenerapplications of a plurality of user computing systems. The program isalso configured to cause the at least one processor to run the log datathrough at least one AI layer. The at least one AI layer is configuredto process the log data and identify a potential RPA process therein.The program is further configured to cause the at least one processor toautomatically generate an RPA workflow including the identified RPAprocess.

In yet another embodiment, a computer-implemented method includesgenerating, by a listener, a log including user interactions with a usercomputing system and sending log data from the log to a server, by thelistener. The log data includes where a user clicks on a screen and inwhich application, keystrokes, which button was clicked, instances ofthe user switching between applications, focus changes, that an emailwas sent and what the email pertains to, or any combination thereof.Additionally, the computer-implemented method includes using the one ormore extracted processes to generate one or more respective workflowsand robots.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of certain embodiments of the inventionwill be readily understood, a more particular description of theinvention briefly described above will be rendered by reference tospecific embodiments that are illustrated in the appended drawings.While it should be understood that these drawings depict only typicalembodiments of the invention and are not therefore to be considered tobe limiting of its scope, the invention will be described and explainedwith additional specificity and detail through the use of theaccompanying drawings, in which:

FIG. 1 is an architectural diagram illustrating an RPA system, accordingto an embodiment of the present invention.

FIG. 2 is an architectural diagram illustrating a deployed RPA system,according to an embodiment of the present invention.

FIG. 3 is an architectural diagram illustrating the relationship betweena designer, activities, and drivers, according to an embodiment of thepresent invention.

FIG. 4 is an architectural diagram illustrating an RPA system, accordingto an embodiment of the present invention.

FIG. 5 is an architectural diagram illustrating a computing systemconfigured to perform AI-based process identification, extraction,and/or automation for RPA, according to an embodiment of the presentinvention.

FIG. 6 is an architectural diagram illustrating a system configured toperform AI-based process identification, extraction, and automation forRPA, according to an embodiment of the present invention.

FIG. 7 is a flowchart illustrating a process for AI-based processidentification, extraction, and automation for RPA, according to anembodiment of the present invention.

FIG. 8 is a flowchart illustrating a process for developing anddeploying RPA workflows based on log data, according to an embodiment ofthe present invention.

FIG. 9 is a flowchart illustrating a process for subject matter expert(SME)-guided process extraction, according to an embodiment of thepresent invention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Some embodiments pertain to AI-based process identification, extraction,and automation for RPA. Listeners may be deployed to user computingsystems to collect data pertaining to user actions. The data collectedby the listeners may then be sent to one or more servers and be storedin a database. This data may be analyzed by AI layers to recognizepatterns of user behavioral processes therein. These recognizedprocesses may then be distilled into respective RPA workflows anddeployed to automate the processes.

These multiple layers of intelligence may thus facilitate the discoveryof new processes to automate and the improvement of already existing anddeployed processes. Thus, some embodiments function as a “process miner”to find processes users perform that are good candidates for automationand then automate these processes. In certain embodiments, the users maynot be aware that the processes are being extracted and automated, andthe data collection and automation may occur without any actions by theusers.

FIG. 1 is an architectural diagram illustrating an RPA system 100,according to an embodiment of the present invention. RPA system 100includes a designer 110 that allows a developer to design and implementworkflows. Designer 110 may provide a solution for applicationintegration, as well as automating third-party applications,administrative Information Technology (IT) tasks, and business ITprocesses. Designer 110 may facilitate development of an automationproject, which is a graphical representation of a business process.Simply put, designer 110 facilitates the development and deployment ofworkflows and robots.

The automation project enables automation of rule-based processes bygiving the developer control of the execution order and the relationshipbetween a custom set of steps developed in a workflow, defined herein as“activities.” One commercial example of an embodiment of designer 110 isUiPath Studio™. Each activity may include an action, such as clicking abutton, reading a file, writing to a log panel, etc. In someembodiments, workflows may be nested or embedded.

Some types of workflows may include, but are not limited to, sequences,flowcharts, Finite State Machines (FSMs), and/or global exceptionhandlers. Sequences may be particularly suitable for linear processes,enabling flow from one activity to another without cluttering aworkflow. Flowcharts may be particularly suitable to more complexbusiness logic, enabling integration of decisions and connection ofactivities in a more diverse manner through multiple branching logicoperators. FSMs may be particularly suitable for large workflows. FSMsmay use a finite number of states in their execution, which aretriggered by a condition (i.e., transition) or an activity. Globalexception handlers may be particularly suitable for determining workflowbehavior when encountering an execution error and for debuggingprocesses.

Once a workflow is developed in designer 110, execution of businessprocesses is orchestrated by conductor 120, which orchestrates one ormore robots 130 that execute the workflows developed in designer 110.One commercial example of an embodiment of conductor 120 is UiPathOrchestrator™. Conductor 120 facilitates management of the creation,monitoring, and deployment of resources in an environment. Conductor 120may act as an integration point with third-party solutions andapplications.

Conductor 120 may manage a fleet of robots 130, connecting and executingrobots 130 from a centralized point. Types of robots 130 that may bemanaged include, but are not limited to, attended robots 132, unattendedrobots 134, development robots (similar to unattended robots 134, butused for development and testing purposes), and nonproduction robots(similar to attended robots 132, but used for development and testingpurposes). Attended robots 132 are triggered by user events and operatealongside a human on the same computing system. Attended robots 132 maybe used with conductor 120 for a centralized process deployment andlogging medium. Attended robots 132 may help the human user accomplishvarious tasks, and may be triggered by user events. In some embodiments,processes cannot be started from conductor 120 on this type of robotand/or they cannot run under a locked screen. In certain embodiments,attended robots 132 can only be started from a robot tray or from acommand prompt. Attended robots 132 should run under human supervisionin some embodiments.

Unattended robots 134 run unattended in virtual environments and canautomate many processes. Unattended robots 134 may be responsible forremote execution, monitoring, scheduling, and providing support for workqueues. Debugging for all robot types may be run in designer 110 in someembodiments. Both attended and unattended robots may automate varioussystems and applications including, but not limited to, mainframes, webapplications, VMs, enterprise applications (e.g., those produced bySAP®, SalesForce®, Oracle®, etc.), and computing system applications(e.g., desktop and laptop applications, mobile device applications,wearable computer applications, etc.).

Conductor 120 may have various capabilities including, but not limitedto, provisioning, deployment, configuration, queueing, monitoring,logging, and/or providing interconnectivity. Provisioning may includecreating and maintenance of connections between robots 130 and conductor120 (e.g., a web application). Deployment may include assuring thecorrect delivery of package versions to assigned robots 130 forexecution. Configuration may include maintenance and delivery of robotenvironments and process configurations. Queueing may include providingmanagement of queues and queue items. Monitoring may include keepingtrack of robot identification data and maintaining user permissions.Logging may include storing and indexing logs to a database (e.g., anSQL database) and/or another storage mechanism (e.g., ElasticSearch®,which provides the ability to store and quickly query large datasets).Conductor 120 may provide interconnectivity by acting as the centralizedpoint of communication for third-party solutions and/or applications.

Robots 130 are execution agents that run workflows built in designer110. One commercial example of some embodiments of robot(s) 130 isUiPath Robots™. In some embodiments, robots 130 install the MicrosoftWindows® Service Control Manager (SCM)-managed service by default. As aresult, such robots 130 can open interactive Windows® sessions under thelocal system account, and have the rights of a Windows® service.

In some embodiments, robots 130 can be installed in a user mode. Forsuch robots 130, this means they have the same rights as the user underwhich a given robot 130 has been installed. This feature may also beavailable for High Density (HD) robots, which ensure full utilization ofeach machine at its maximum potential. In some embodiments, any type ofrobot 130 may be configured in an HD environment.

Robots 130 in some embodiments are split into several components, eachbeing dedicated to a particular automation task. The robot components insome embodiments include, but are not limited to, SCM-managed robotservices, user mode robot services, executors, agents, and command line.SCM-managed robot services manage and monitor Windows® sessions and actas a proxy between conductor 120 and the execution hosts (i.e., thecomputing systems on which robots 130 are executed). These services aretrusted with and manage the credentials for robots 130. A consoleapplication is launched by the SCM under the local system.

User mode robot services in some embodiments manage and monitor Windows®sessions and act as a proxy between conductor 120 and the executionhosts. User mode robot services may be trusted with and manage thecredentials for robots 130. A Windows® application may automatically belaunched if the SCM-managed robot service is not installed.

Executors may run given jobs under a Windows® session (i.e., they mayexecute workflows. Executors may be aware of per-monitor dots per inch(DPI) settings. Agents may be Windows® Presentation Foundation (WPF)applications that display the available jobs in the system tray window.Agents may be a client of the service. Agents may request to start orstop jobs and change settings. The command line is a client of theservice. The command line is a console application that can request tostart jobs and waits for their output.

Having components of robots 130 split as explained above helpsdevelopers, support users, and computing systems more easily run,identify, and track what each component is executing. Special behaviorsmay be configured per component this way, such as setting up differentfirewall rules for the executor and the service. The executor may alwaysbe aware of DPI settings per monitor in some embodiments. As a result,workflows may be executed at any DPI, regardless of the configuration ofthe computing system on which they were created. Projects from designer110 may also be independent of browser zoom level in some embodiments.For applications that are DPI-unaware or intentionally marked asunaware, DPI may be disabled in some embodiments.

FIG. 2 is an architectural diagram illustrating a deployed RPA system200, according to an embodiment of the present invention. In someembodiments, RPA system 200 may be, or may be a part of, RPA system 100of FIG. 1. It should be noted that the client side, the server side, orboth, may include any desired number of computing systems withoutdeviating from the scope of the invention. On the client side, a robotapplication 210 includes executors 212, an agent 214, and a designer216. However, in some embodiments, designer 216 may not be running oncomputing system 210. Executors 212 are running processes. Severalbusiness projects may run simultaneously, as shown in FIG. 2. Agent 214(e.g., a Windows® service) is the single point of contact for allexecutors 212 in this embodiment. All messages in this embodiment arelogged into conductor 230, which processes them further via databaseserver 240, indexer server 250, or both. As discussed above with respectto FIG. 1, executors 212 may be robot components.

In some embodiments, a robot represents an association between a machinename and a username. The robot may manage multiple executors at the sametime. On computing systems that support multiple interactive sessionsrunning simultaneously (e.g., Windows® Server 2012), there may bemultiple robots running at the same time, each in a separate Windows®session using a unique username. This is referred to as HD robots above.

Agent 214 is also responsible for sending the status of the robot (e.g.,periodically sending a “heartbeat” message indicating that the robot isstill functioning) and downloading the required version of the packageto be executed. The communication between agent 214 and conductor 230 isalways initiated by agent 214 in some embodiments. In the notificationscenario, agent 214 may open a WebSocket channel that is later used byconductor 230 to send commands to the robot (e.g., start, stop, etc.).

On the server side, a presentation layer (web application 232, Open DataProtocol (OData) Representative State Transfer (REST) ApplicationProgramming Interface (API) endpoints 234, and notification andmonitoring 236), a service layer (API implementation/business logic238), and a persistence layer (database server 240 and indexer server250) are included. Conductor 230 includes web application 232, ODataREST API endpoints 234, notification and monitoring 236, and APIimplementation/business logic 238. In some embodiments, most actionsthat a user performs in the interface of conductor 220 (e.g., viabrowser 220) are performed by calling various APIs. Such actions mayinclude, but are not limited to, starting jobs on robots,adding/removing data in queues, scheduling jobs to run unattended, etc.without deviating from the scope of the invention. Web application 232is the visual layer of the server platform. In this embodiment, webapplication 232 uses Hypertext Markup Language (HTML) and JavaScript(JS). However, any desired markup languages, script languages, or anyother formats may be used without deviating from the scope of theinvention. The user interacts with web pages from web application 232via browser 220 in this embodiment in order to perform various actionsto control conductor 230. For instance, the user may create robotgroups, assign packages to the robots, analyze logs per robot and/or perprocess, start and stop robots, etc.

In addition to web application 232, conductor 230 also includes servicelayer that exposes OData REST API endpoints 234. However, otherendpoints may be included without deviating from the scope of theinvention. The REST API is consumed by both web application 232 andagent 214. Agent 214 is the supervisor of one or more robots on theclient computer in this embodiment.

The REST API in this embodiment covers configuration, logging,monitoring, and queueing functionality. The configuration endpoints maybe used to define and configure application users, permissions, robots,assets, releases, and environments in some embodiments. Logging RESTendpoints may be used to log different information, such as errors,explicit messages sent by the robots, and other environment-specificinformation, for instance. Deployment REST endpoints may be used by therobots to query the package version that should be executed if the startjob command is used in conductor 230. Queueing REST endpoints may beresponsible for queues and queue item management, such as adding data toa queue, obtaining a transaction from the queue, setting the status of atransaction, etc.

Monitoring REST endpoints may monitor web application 232 and agent 214.Notification and monitoring API 236 may be REST endpoints that are usedfor registering agent 214, delivering configuration settings to agent214, and for sending/receiving notifications from the server and agent214. Notification and monitoring API 236 may also use WebSocketcommunication in some embodiments.

The persistence layer includes a pair of servers in thisembodiment—database server 240 (e.g., a SQL server) and indexer server250. Database server 240 in this embodiment stores the configurations ofthe robots, robot groups, associated processes, users, roles, schedules,etc. This information is managed through web application 232 in someembodiments. Database server 240 may manages queues and queue items. Insome embodiments, database server 240 may store messages logged by therobots (in addition to or in lieu of indexer server 250).

Indexer server 250, which is optional in some embodiments, stores andindexes the information logged by the robots. In certain embodiments,indexer server 250 may be disabled through configuration settings. Insome embodiments, indexer server 250 uses ElasticSearch®, which is anopen source project full-text search engine. Messages logged by robots(e.g., using activities like log message or write line) may be sentthrough the logging REST endpoint(s) to indexer server 250, where theyare indexed for future utilization.

FIG. 3 is an architectural diagram illustrating the relationship 300between a designer 310, activities 320, 330, and drivers 340, accordingto an embodiment of the present invention. Per the above, a developeruses designer 310 to develop workflows that are executed by robots.Workflows may include user-defined activities 320 and UI automationactivities 330. Some embodiments are able to identify non-textual visualcomponents in an image, which is called computer vision (CV) herein.Some CV activities pertaining to such components may include, but arenot limited to, click, type, get text, hover, element exists, refreshscope, highlight, etc. Click in some embodiments identifies an elementusing CV, optical character recognition (OCR), fuzzy text matching, andmulti-anchor, for example, and clicks it. Type may identify an elementusing the above and types in the element. Get text may identify thelocation of specific text and scan it using OCR. Hover may identify anelement and hover over it. Element exists may check whether an elementexists on the screen using the techniques described above. In someembodiments, there may be hundreds or even thousands of activities thatcan be implemented in designer 310. However, any number and/or type ofactivities may be available without deviating from the scope of theinvention.

UI automation activities 330 are a subset of special, lower levelactivities that are written in lower level code (e.g., CV activities)and facilitate interactions with the screen. UI automation activities330 facilitate these interactions via drivers 340 that allow the robotto interact with the desired software. For instance, drivers 340 mayinclude OS drivers 342, browser drivers 344, VM drivers 346, enterpriseapplication drivers 348, etc.

Drivers 340 may interact with the OS at a low level looking for hooks,monitoring for keys, etc. They may facilitate integration with Chrome®,IE®, Citrix®, SAP®, etc. For instance, the “click” activity performs thesame role in these different applications via drivers 340.

FIG. 4 is an architectural diagram illustrating an RPA system 400,according to an embodiment of the present invention. In someembodiments, RPA system 400 may be or include RPA systems 100 and/or 200of FIGS. 1 and/or 2. RPA system 400 includes multiple client computingsystems 410 running robots. Computing systems 410 are able tocommunicate with a conductor computing system 420 via a web applicationrunning thereon. Conductor computing system 420, in turn, is able tocommunicate with a database server 430 and an optional indexer server440.

With respect to FIGS. 1 and 3, it should be noted that while a webapplication is used in these embodiments, any suitable client/serversoftware may be used without deviating from the scope of the invention.For instance, the conductor may run a server-side application thatcommunicates with non-web-based client software applications on theclient computing systems.

FIG. 5 is an architectural diagram illustrating a computing system 500configured to perform AI-based process identification, extraction,and/or automation for RPA, according to an embodiment of the presentinvention. In some embodiments, computing system 500 may be one or moreof the computing systems depicted and/or described herein. Computingsystem 500 includes a bus 505 or other communication mechanism forcommunicating information, and processor(s) 510 coupled to bus 505 forprocessing information. Processor(s) 510 may be any type of general orspecific purpose processor, including a Central Processing Unit (CPU),an Application Specific Integrated Circuit (ASIC), a Field ProgrammableGate Array (FPGA), a Graphics Processing Unit (GPU), multiple instancesthereof, and/or any combination thereof. Processor(s) 510 may also havemultiple processing cores, and at least some of the cores may beconfigured to perform specific functions. Multi-parallel processing maybe used in some embodiments. In certain embodiments, at least one ofprocessor(s) 510 may be a neuromorphic circuit that includes processingelements that mimic biological neurons. In some embodiments,neuromorphic circuits may not require the typical components of a VonNeumann computing architecture.

Computing system 500 further includes a memory 515 for storinginformation and instructions to be executed by processor(s) 510. Memory515 can be comprised of any combination of Random Access Memory (RAM),Read Only Memory (ROM), flash memory, cache, static storage such as amagnetic or optical disk, or any other types of non-transitorycomputer-readable media or combinations thereof. Non-transitorycomputer-readable media may be any available media that can be accessedby processor(s) 510 and may include volatile media, non-volatile media,or both. The media may also be removable, non-removable, or both.

Additionally, computing system 500 includes a communication device 520,such as a transceiver, to provide access to a communications network viaa wireless and/or wired connection. In some embodiments, communicationdevice 520 may be configured to use Frequency Division Multiple Access(FDMA), Single Carrier FDMA (SC-FDMA), Time Division Multiple Access(TDMA), Code Division Multiple Access (CDMA), Orthogonal FrequencyDivision Multiplexing (OFDM), Orthogonal Frequency Division MultipleAccess (OFDMA), Global System for Mobile (GSM) communications, GeneralPacket Radio Service (GPRS), Universal Mobile Telecommunications System(UMTS), cdma2000, Wideband CDMA (W-CDMA), High-Speed Downlink PacketAccess (HSDPA), High-Speed Uplink Packet Access (HSUPA), High-SpeedPacket Access (HSPA), Long Term Evolution (LTE), LTE Advanced (LTE-A),802.11x, Wi-Fi, Zigbee, Ultra-WideBand (UWB), 802.16x, 802.15, HomeNode-B (HnB), Bluetooth, Radio Frequency Identification (RFID), InfraredData Association (IrDA), Near-Field Communications (NFC), fifthgeneration (5G), New Radio (NR), any combination thereof, and/or anyother currently existing or future-implemented communications standardand/or protocol without deviating from the scope of the invention. Insome embodiments, communication device 520 may include one or moreantennas that are singular, arrayed, phased, switched, beamforming,beamsteering, a combination thereof, and or any other antennaconfiguration without deviating from the scope of the invention.

Processor(s) 510 are further coupled via bus 505 to a display 525, suchas a plasma display, a Liquid Crystal Display (LCD), a Light EmittingDiode (LED) display, a Field Emission Display (FED), an Organic LightEmitting Diode (OLED) display, a flexible OLED display, a flexiblesubstrate display, a projection display, a 4K display, a high definitiondisplay, a Retina® display, an In-Plane Switching (IPS) display, or anyother suitable display for displaying information to a user. Display 525may be configured as a touch (haptic) display, a three dimensional (3D)touch display, a multi-input touch display, a multi-touch display, etc.using resistive, capacitive, surface-acoustic wave (SAW) capacitive,infrared, optical imaging, dispersive signal technology, acoustic pulserecognition, frustrated total internal reflection, etc. Any suitabledisplay device and haptic I/O may be used without deviating from thescope of the invention.

A keyboard 530 and a cursor control device 535, such as a computermouse, a touchpad, etc., are further coupled to bus 505 to enable a userto interface with computing system 500. However, in certain embodiments,a physical keyboard and mouse may not be present, and the user mayinteract with the device solely through display 525 and/or a touchpad(not shown). Any type and combination of input devices may be used as amatter of design choice. In certain embodiments, no physical inputdevice and/or display is present. For instance, the user may interactwith computing system 500 remotely via another computing system incommunication therewith, or computing system 500 may operateautonomously.

Memory 515 stores software modules that provide functionality whenexecuted by processor(s) 510. The modules include an operating system540 for computing system 500. The modules further include a processidentification, extraction, and automation module 545 that is configuredto perform all or part of the processes described herein or derivativesthereof. Computing system 500 may include one or more additionalfunctional modules 550 that include additional functionality.

One skilled in the art will appreciate that a “system” could be embodiedas a server, an embedded computing system, a personal computer, aconsole, a personal digital assistant (PDA), a cell phone, a tabletcomputing device, a quantum computing system, or any other suitablecomputing device, or combination of devices without deviating from thescope of the invention. Presenting the above-described functions asbeing performed by a “system” is not intended to limit the scope of thepresent invention in any way, but is intended to provide one example ofthe many embodiments of the present invention. Indeed, methods, systems,and apparatuses disclosed herein may be implemented in localized anddistributed forms consistent with computing technology, including cloudcomputing systems.

It should be noted that some of the system features described in thisspecification have been presented as modules, in order to moreparticularly emphasize their implementation independence. For example, amodule may be implemented as a hardware circuit comprising custom verylarge scale integration (VLSI) circuits or gate arrays, off-the-shelfsemiconductors such as logic chips, transistors, or other discretecomponents. A module may also be implemented in programmable hardwaredevices such as field programmable gate arrays, programmable arraylogic, programmable logic devices, graphics processing units, or thelike.

A module may also be at least partially implemented in software forexecution by various types of processors. An identified unit ofexecutable code may, for instance, include one or more physical orlogical blocks of computer instructions that may, for instance, beorganized as an object, procedure, or function. Nevertheless, theexecutables of an identified module need not be physically locatedtogether, but may include disparate instructions stored in differentlocations that, when joined logically together, comprise the module andachieve the stated purpose for the module. Further, modules may bestored on a computer-readable medium, which may be, for instance, a harddisk drive, flash device, RAM, tape, and/or any other suchnon-transitory computer-readable medium used to store data withoutdeviating from the scope of the invention.

Indeed, a module of executable code could be a single instruction, ormany instructions, and may even be distributed over several differentcode segments, among different programs, and across several memorydevices. Similarly, operational data may be identified and illustratedherein within modules, and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single data set, or may be distributed overdifferent locations including over different storage devices, and mayexist, at least partially, merely as electronic signals on a system ornetwork.

FIG. 6 is an architectural diagram illustrating a system 600 configuredto perform AI-based process identification, extraction, and automationfor RPA, according to an embodiment of the present invention. System 600includes user computing systems, such as desktop computer 602, tablet604, and smart phone 606. However, any desired computing system may beused without deviating from the scope of invention including, but notlimited to, smart watches, laptop computers, Internet-of-Things (IoT)devices, vehicle computing systems, etc.

Each computing system 602, 604, 606 has a listener 610 installedthereon. Listeners 610 may be robots generated via an RPA designerapplication, part of an operating system, a downloadable application fora personal computer (PC) or smart phone, or any other software and/orhardware without deviating from the scope of the invention. Indeed, insome embodiments, the logic of one or more of listeners 610 isimplemented partially or completely via physical hardware.

Listeners 610 generate logs of user interactions with the respectivecomputing system 602, 604, 606 and send the log data via a network 620(e.g., a local area network (LAN), a mobile communications network, asatellite communications network, the Internet, any combination thereof,etc.) to a server 630. The data that is logged may include, but is notlimited to, which buttons were clicked, where a mouse was moved, thetext that was entered in a field, that one window was minimized andanother was opened, the application associated with a window, etc. Insome embodiments, server 630 may run a conductor application and thedata may be sent periodically as part of the heartbeat message. Incertain embodiments, the log data may be sent to server 630 once apredetermined amount of log data has been collected, after apredetermined time period has elapsed, or both. Server 630 stores thereceived log data from listeners 610 in a database 640.

When instructed by a human user (e.g., an RPA engineer or a datascientist), when a predetermined amount of log data has been collected,when a predetermined amount of time has passed since the last analysis,etc., server 630 accesses log data collected from various users bylisteners 610 from database 640 and runs the log data through multipleAI layers 632. AI layers 632 process the log data and identify one ormore potential processes therein. AI layers 632 may perform statisticalmodeling (e.g., hidden Markov models (HMMs)) and utilize deep learningtechniques (e.g., long short term memory (LSTM) deep learning, encodingof previous hidden states, etc.) and perform case identification toidentify an atomic instance of a process. For invoice processing, forexample, completion of one invoice may be a case. The system thusdetermines where one case finishes and the next case begins. Opening anemail may be the start of a case, for example, and the patterns of thecases may be analyzed to determine variations and commonalities.

In some embodiments, identified processes may be listed for a user toperuse, and may be sorted by various factors including, but not limitedto, an RPA score indicating how suitable a given process is to RPA(e.g., based on complexity of the automation, execution time, perceivedbenefit to key performance indicators such as revenue generated, revenuesaved, time saved, etc.), process name, total recording time, the numberof users who executed the process, process execution time (e.g., leastor most time), etc. The process workflow may be displayed when a userclicks on a given process, including steps, parameters, andinterconnections. In certain embodiments, only process activities thatappear to be important from a clustering perspective may be used.

If a similar process already exists, server 630 may identify thissimilarity and know that the identified process should replace anexisting process for a similar automation that works less optimally. Forexample, similarities between processes may be determined by a commonbeginning and end and some amount of statistical commonality in thesteps taking in between. Commonality may be determined by entropy,minimization of a process detection objective function, etc. Theobjective function threshold may be set automatically in someembodiments, and this may be modified during training if processes thatwere identified as dissimilar by the system are indicated as beingsimilar by a user. Server 630 may then automatically generate a workflowincluding the identified process, generate a robot implementing theworkflow (or a replacement robot), and push the generated robot out touser computing systems 602, 604, 606 to be executed thereon.

Alternatively, in certain embodiments, suggested processes from AIlayers 632 may be presented to an RPA engineer via a designerapplication 652 on a computing system 650. The RPA engineer can thenreview the workflow, make any desired changes, and then deploy theworkflow via a robot to computing systems 602, 604, 606, or cause therobot to be deployed. For example, deployment may occur via a conductorapplication running on server 630 or another server, which may push arobot implementing the process out to user computing systems 602, 604,606. In some embodiments, this workflow deployment may be realized viaautomation manager functionality in the designer application, and theRPA engineer may merely click a button to implement the process in arobot.

Listeners

In order to extract data pertaining to actions taken by users oncomputing systems 602, 604, 606, listeners 610 may be employed on theclient side at the driver level (e.g., drivers 340 of FIG. 3) to extractdata from whitelisted applications. For example, listeners 610 mayrecord where a user clicked on the screen and in what application,keystrokes, which button was clicked, instances of the user switchingbetween applications, focus changes, that an email was sent and what theemail pertains to, etc. Such data can be used to generate ahigh-fidelity log of the user's interactions with computing systems 602,604, 606.

In some embodiments, data may be generated until a desired per-user ortotal volume of data and/or a maximum recording time (per user or total)is reached. This may constitute a recording goal that may be set for anindividual user, for multiple users, or both. In certain embodiments,listeners 610 may stop recording once an individual or group datarecording goal is reached. The data may then be uploaded to server 630and stored in database 640.

In certain embodiments, applications for which user interactions are tobe logged may be whitelisted. In other words, only interactions withcertain specified applications may be of interest. For instance,interactions with a web browser and an email application may berecorded, but interactions with other applications may be ignored.

In addition to or alternatively to generating log data for processextraction, some embodiments may provide insights into what users areactually doing. For instance, listeners 610 may determine whichapplications the users are actually using, what percentage of the timeusers are using a given application, which features within theapplication the users are using and which they are not, etc. Thisinformation may be provided to a manager to make informed decisionsregarding whether to renew a license for an application, whether to notrenew a license for a feature or downgrade to a less expensive versionthat lacks the feature, whether a user is not using applications thattend to make other employees more productive so the user can be trainedappropriately, whether a user spends a large amount of time performingnon-work activities (e.g., checking personal email or surfing the web)or away from his or her desk (e.g., not interacting with the computingsystem), etc.

In some embodiments, detection updates can be pushed to the listeners toimprove their driver-level user interaction detection and captureprocesses. In certain embodiments, listeners 610 may employ AI in theirdetection. In certain embodiments, robots implementing processes fromautomation workflows may automatically be pushed to computing systems602, 604, 606 via respective listeners 610.

AI Layers

In some embodiments, multiple AI layers may be used. Each AI layer is analgorithm (or model) that runs on the log data, and the AI model itselfmay be deep learning neural networks (DLNNs) of trained artificial“neurons” that are trained in training data. Layers may be run in seriesor in parallel.

The AI layers may include, but are not limited to, a sequence extractionlayer, a clustering detection layer, a visual component detection layer,a text recognition layer (e.g., OCR), an audio-to-text translationlayer, or any combination thereof. However, any desired number andtype(s) of layers may be used without deviating from the scope of theinvention. Using multiple layers may allow the system to develop aglobal picture of what is happening in a screen or process. For example,one AI layer could perform OCR, another could detect buttons, etc.

Patterns may be determined individually by an AI layer or collectivelyby multiple AI layers. A probability or an output in terms of a useraction could be used. For instance, to determine the particulars of abutton, its text, where a user clicked, etc., the system may need toknow where the button is, its text, the positioning on the screen, etc.

Subject Matter Expert-Guided Process Extraction

In some embodiments, such as those where the user is interacting with aseries of images provided by a virtual machine environment and theactual software with which the user is interacting is executed remotely,or where driver-level data from listeners 610 does not accurately orentirely capture what the user is doing, listeners 610 may capturescreenshots of what the user is doing at certain times (e.g., with apredetermined frequency, when a user takes a certain action, acombination thereof, etc.). A subject matter expert (SME) may thenreview recorded screenshots, save the relevant screenshots, and deletethose that are not relevant.

After the relevant screenshots are identified, these screenshots maythen be fed through a trained computer vision (CV) model (e.g., executedby server 630 or locally on the user's computing system) that uses AI toidentify what the user was doing in the video. Additionally oralternatively, the screenshots may be used to train the CV model formore accurate detection. Once many actions are obtained from multipleusers, the identified actions (e.g., clicked buttons, applications thatwere used, text that was entered, etc.) may then be fed to AI layers 632to extract processes therefrom. Alternatively, extracted actions from asingle user's screenshots could be used to automatically generate aworkflow, which the SME may then edit to ensure it is correct in someembodiments. This information may be used in certain embodiments toproduce a product definition document (PDD) that contains in substantialdetail steps, screenshots, and flowchart(s). Such a PDD could be usedfor product documentation, for example.

In certain embodiments, scene changes may be detected to produce thePDD. For instance, 20 different activities that a user is performing maybe different scenes. The system may then use this information and logdata to generate a skeleton robot that can be imported into a developerapplication, there the developer can then flush out the workflowcontents.

FIG. 7 is a flowchart illustrating a process 700 for AI-based processidentification, extraction, and automation for RPA, according to anembodiment of the present invention. The process begins with listenerapplications (i.e., listeners) generating generate logs of userinteractions with their respective computing systems at 710. In someembodiments, the listeners are configured to record where a user clickson the screen and in what application, keystrokes, which button wasclicked, instances of the user switching between applications, focuschanges, that an email was sent and what the email pertains to, or anycombination thereof. In certain embodiments, the listeners areconfigured to determine which applications the users are actually using,what percentage of time the users are using a given application, whichfeatures within the applications the users are using, which features theusers are not using, or any combination thereof. In some embodiments,the listeners are configured to employ AI in detecting user interactionswith their computing systems.

In some embodiments, one or more (or potentially all) of the usercomputing systems are servers. For example, it may be desirable toextract processes for server management by deploying a listener on theserver to monitor load balancing, server performance, corrective actionstaken by administrators when server issues arise, etc. In this manner,robots may be trained to mimic administrators and automatically remedyissues that administrators typically need to address themselves.

The listeners send the generated log data to the server at 720. In someembodiments, the server or another server runs a conductor applicationand the log data is sent periodically to the conductor application aspart of a heartbeat message. This data may include, but is not limitedto, robot uptime, the workflow that is being executed, data pertainingto workflow activities (e.g., a detected total from an invoice) thatcould be used by a conductor application to provide certain globalinformation in a dashboard (e.g., a total dollar amount processed by agroup of robots or all robots of a certain type), etc. The heartbeatmessage may be sent every 3 seconds, every 5-10 seconds, every minute,or any other suitable time period or range without deviating from thescope of the invention. In certain embodiments, the log data is sent tothe server once a predetermined amount of log data has been collected,after a predetermined time period has elapsed, or both. The log data isthen stored in a database at 730.

The server accesses the stored log data collected from the listeners andruns the log data through at least one AI layer at 740. In someembodiments, multiple AI layers may be used including, but not limitedto, a sequence extraction layer, a clustering detection layer, a visualcomponent detection layer, a text recognition layer, an audio-to-texttranslation layer, and/or any combination thereof. The AI layer(s) areconfigured to process the log data and identify potential RPAprocess(es) therein at 750. More specifically, the identified RPAprocesses are those capable of automating certain user actions whenimplemented in an RPA workflow. The server is configured to identifysimilarities in existing processes implemented by existing robots (ifany) at 760. For example, similarities between processes may bedetermined by a common beginning and end and some amount of statisticalcommonality in the steps taking in between. The server is thenconfigured to automatically generate workflow(s) including theidentified RPA process(es) at 770, generate robot(s) implementing theworkflow(s) at 780, and deploy the generated robot(s) at 790 (e.g., bypushing them out to the user's computing systems and executing therobots thereon). If a similar process is implemented by an existingrobot, step 790 may include replacing the existing robot with the newlygenerated robot.

FIG. 8 is a flowchart illustrating a process for developing anddeploying RPA workflows based on log data, according to an embodiment ofthe present invention. The process begins processing data via AIlayer(s) to identify potential RPA process(es) at 810. The identifiedprocess(es) are then presented to an RPA developer via a designerapplication on a computing system at 820. The designer applicationreceives and implements changes to the workflow made by the RPAdeveloper at 830. The designer application then generates a robot fromthe workflow at 840, and the robot is deployed at 850. In someembodiments, deployment may occur via a conductor application running ona server, which is configured to push the robot out to user computingsystems. In some embodiments, this workflow deployment is realized viaautomation manager functionality in the designer application thatimplements the identified process in the robot responsive to input fromthe RPA developer.

FIG. 9 is a flowchart illustrating a process for subject matter expert(SME)-guided process extraction, according to an embodiment of thepresent invention. The process begins with a listener capturingscreenshots from a user's computing system while the user interacts withthe computing system at 910. In some embodiments, the screenshots may becaptured with a predetermined frequency, when a user takes a certainaction, or a combination thereof. In certain embodiments, the savedscreenshots are part of a video. Screenshots identified by an SME asbeing relevant are saved at 920 and screenshots identified as not beingrelevant are deleted at 930. These screenshots may be identified isirrelevant and discarded by the SME, by a trained AI model, by ananomaly detector looking for screenshots that are dissimilar to othersthat have been identified as relevant, etc.

The saved screenshots are then fed through a trained CV model that usesAI to identify what the user was doing in the saved screenshots at 940.The saved screenshots are also used to train the CV model for moreaccurate detection at 950. Once actions are identified by the CV model,these actions are fed to one or more AI layers to extract processestherefrom at 960. The extracted process(es) are then used to generaterespective workflows(s) and robot(s) at 970.

The process steps performed in FIGS. 7-9 may be performed by a computerprogram, encoding instructions for the processor(s) to perform at leastpart of the process(es) described in FIGS. 7-9, in accordance withembodiments of the present invention. The computer program may beembodied on a non-transitory computer-readable medium. Thecomputer-readable medium may be, but is not limited to, a hard diskdrive, a flash device, RAM, a tape, and/or any other such medium orcombination of media used to store data. The computer program mayinclude encoded instructions for controlling processor(s) of a computingsystem (e.g., processor(s) 510 of computing system 500 of FIG. 5) toimplement all or part of the process steps described in FIGS. 7-9, whichmay also be stored on the computer-readable medium.

The computer program can be implemented in hardware, software, or ahybrid implementation. The computer program can be composed of modulesthat are in operative communication with one another, and which aredesigned to pass information or instructions to display. The computerprogram can be configured to operate on a general purpose computer, anASIC, or any other suitable device.

It will be readily understood that the components of various embodimentsof the present invention, as generally described and illustrated in thefigures herein, may be arranged and designed in a wide variety ofdifferent configurations. Thus, the detailed description of theembodiments of the present invention, as represented in the attachedfigures, is not intended to limit the scope of the invention as claimed,but is merely representative of selected embodiments of the invention.

The features, structures, or characteristics of the invention describedthroughout this specification may be combined in any suitable manner inone or more embodiments. For example, reference throughout thisspecification to “certain embodiments,” “some embodiments,” or similarlanguage means that a particular feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment of the present invention. Thus, appearances of the phrases“in certain embodiments,” “in some embodiment,” “in other embodiments,”or similar language throughout this specification do not necessarily allrefer to the same group of embodiments and the described features,structures, or characteristics may be combined in any suitable manner inone or more embodiments.

It should be noted that reference throughout this specification tofeatures, advantages, or similar language does not imply that all of thefeatures and advantages that may be realized with the present inventionshould be or are in any single embodiment of the invention. Rather,language referring to the features and advantages is understood to meanthat a specific feature, advantage, or characteristic described inconnection with an embodiment is included in at least one embodiment ofthe present invention. Thus, discussion of the features and advantages,and similar language, throughout this specification may, but do notnecessarily, refer to the same embodiment.

Furthermore, the described features, advantages, and characteristics ofthe invention may be combined in any suitable manner in one or moreembodiments. One skilled in the relevant art will recognize that theinvention can be practiced without one or more of the specific featuresor advantages of a particular embodiment. In other instances, additionalfeatures and advantages may be recognized in certain embodiments thatmay not be present in all embodiments of the invention.

One having ordinary skill in the art will readily understand that theinvention as discussed above may be practiced with steps in a differentorder, and/or with hardware elements in configurations which aredifferent than those which are disclosed. Therefore, although theinvention has been described based upon these preferred embodiments, itwould be apparent to those of skill in the art that certainmodifications, variations, and alternative constructions would beapparent, while remaining within the spirit and scope of the invention.In order to determine the metes and bounds of the invention, therefore,reference should be made to the appended claims.

In an embodiment, a system includes a server and a plurality of usercomputing systems comprising respective listener applications. Thelistener applications are configured to generate logs of userinteractions with their respective user computing systems and send thelog data to the server. The server is configured to access log datacollected from the listeners and run the log data through at least oneAI layer. In some embodiments, multiple AI layers may be used including,but not limited to, a sequence extraction layer, a clustering detectionlayer, a visual component detection layer, a text recognition layer, anaudio-to-text translation layer, and/or any combination thereof. The atleast one AI layer is configured to process the log data and identify apotential RPA process therein. The server is then configured toautomatically generate a workflow including the identified RPA process,generate a robot implementing the workflow, and push the generated robotout to the user computing systems to be executed thereon.

In some embodiments, the server or another server runs a conductorapplication and the log data is sent periodically to the conductorapplication as part of a heartbeat message. In certain embodiments, thelog data is sent to the server once a predetermined amount of log datahas been collected, after a predetermined time period has elapsed, orboth. In some embodiments, the server stores the received log data fromthe listeners in a database. In certain embodiments, the server isconfigured to find similarities between a previously existing RPAprocess executed by a robot on the user computing systems and replacethe previously existing robot with the generated robot.

In some embodiments, the listeners are configured to record where a userclicks on the screen and in what application, keystrokes, which buttonwas clicked, instances of the user switching between applications, focuschanges, that an email was sent and what the email pertains to, or anycombination thereof. In certain embodiments, the listeners areconfigured to determine which applications the users are actually using,what percentage of time the users are using a given application, whichfeatures within the applications the users are using, which features theusers are not using, or any combination thereof. In some embodiments,the listeners are configured to employ AI in detecting user interactionswith their computing systems.

In another embodiment, a computer-implemented method includes processingdata, by a server, via at least one AI layer to identify one or morepotential RPA processes. The computer-implemented method also includesreceiving the identified potential RPA processes and presenting the oneor more identified RPA processes to a developer, via a designerapplication on a developer computing system. The computer-implementedmethod further includes receiving and implementing changes to theworkflow, by the designer application. Additionally, thecomputer-implemented method includes generating a robot from theworkflow, by the designer application. The robot may then be deployed toone or more user computing systems. In some embodiments, deployment mayoccur via a conductor application running on a server, which isconfigured to push the robot out to user computing systems. In someembodiments, this workflow deployment is realized via automation managerfunctionality in the designer application that implements the identifiedprocess in the robot responsive to input from the RPA developer.

In yet another embodiment, a computer-implemented method includescapturing screenshots, by a listener on a user computing system, while auser interacts with the user computing system. In some embodiments, thescreenshots may be captured with a predetermined frequency, when a usertakes a certain action, or a combination thereof. In certainembodiments, the saved screenshots are part of a video. Thecomputer-implemented method also includes saving screenshots of thecaptured screenshots that are marked as relevant and deletingscreenshots of the captured screenshots identified as irrelevant. Thecomputer implemented method further includes feeding the savedscreenshots through a trained CV model that uses AI to identify what theuser was doing in the saved screenshots and feeding the identifiedactions to one or more AI layers to extract one or more processestherefrom. In some embodiments, the saved screenshots are also used totrain the CV model for more accurate detection in the future.Additionally, the computer-implemented method includes using the one ormore extracted processes to generate one or more respective workflowsand robots.

The invention claimed is:
 1. A system, comprising: a server; and aplurality of user computing systems comprising respective listenerapplications, the listener applications configured to generate logscomprising user interactions with respective user computing systems andsend log data from the logs to the server, wherein the server isconfigured to: access log data collected from the listeners and run thelog data through at least one artificial intelligence (AI) layer, the atleast one AI layer configured to process the log data and identify apotential robotic process automation (RPA) process therein, andautomatically generate an RPA workflow including the identified RPAprocess.
 2. The system of claim 1, wherein the server is furtherconfigured to: generate a robot implementing the RPA workflow; and pushthe generated robot out to the user computing systems to be executedthereon.
 3. The system of claim 1, wherein the at least one AI layercomprises a sequence extraction layer, a clustering detection layer, avisual component detection layer, a text recognition layer, anaudio-to-text translation layer, or any combination thereof.
 4. Thesystem of claim 1, wherein the server comprises a conductor applicationand the log data is sent periodically to the conductor application bythe listeners of the plurality of user computing systems as part of aheartbeat message.
 5. The system of claim 1, wherein the log data issent to the server once a predetermined amount of log data has beencollected, after a predetermined time period has elapsed, or both. 6.The system of claim 1, further comprising: a database communicablycoupled to the server, wherein the server is configured to store thereceived log data from the listeners in the database.
 7. The system ofclaim 1, wherein the server is further configured to: determinesimilarities between the identified RPA process and a previouslyexisting RPA process executed by a robot on the user computing systems;and replace an RPA workflow of the robot executing the previouslyexisting RPA process with the identified RPA process.
 8. The system ofclaim 7, wherein similarities between the identified RPA process and thepreviously existing RPA process are determined by a common beginning, acommon end, and a predetermined amount of statistical commonality insteps taking in between in the RPA processes.
 9. The system of claim 1,wherein the listeners are configured to record where a user clicks on ascreen and in which application, keystrokes, which button was clicked,instances of the user switching between applications, focus changes,that an email was sent and what the email pertains to, or anycombination thereof.
 10. The system of claim 1, wherein the listenersare configured to determine which applications users of the usercomputing systems are using, what percentage of time the users are usinga given application, which features within the applications the usersare using, which features within the applications that the users are notusing, or any combination thereof.
 11. A non-transitorycomputer-readable medium storing a computer program, the computerprogram configured to cause at least one processor to: access log datacollected from respective listener applications of a plurality of usercomputing systems; run the log data through at least one artificialintelligence (AI) layer, the at least one AI layer configured to processthe log data and identify a potential robotic process automation (RPA)process therein, and automatically generate an RPA workflow includingthe identified RPA process.
 12. The non-transitory computer-readablemedium of claim 11, wherein the computer program is further configuredto cause the at least one processor to: generate a robot implementingthe RPA workflow; and push the generated robot out to the user computingsystems to be executed thereon.
 13. The non-transitory computer-readablemedium of claim 11, wherein the at least one AI layer comprises asequence extraction layer, a clustering detection layer, a visualcomponent detection layer, a text recognition layer, an audio-to-texttranslation layer, or any combination thereof.
 14. The non-transitorycomputer-readable medium of claim 11, wherein the computer program isfurther configured to cause the at least one processor to: determinesimilarities between the identified RPA process and a previouslyexisting RPA process executed by a robot on the user computing systems;and replace an RPA workflow of the robot executing the previouslyexisting RPA process with the identified RPA process.
 15. Thenon-transitory computer-readable medium of claim 11, whereinsimilarities between the identified RPA process and the previouslyexisting RPA process are determined by a common beginning, a common end,and a predetermined amount of statistical commonality in steps taking inbetween in the RPA processes.
 16. A computer-implemented method,comprising: generating, by a listener, a log comprising userinteractions with a user computing system; and sending log data from thelog to a server, by the listener, wherein the log data comprises where auser clicks on a screen and in which application, keystrokes, whichbutton was clicked, instances of the user switching betweenapplications, focus changes, that an email was sent and what the emailpertains to, or any combination thereof.
 17. The computer-implementedmethod of claim 16, wherein the log data is sent periodically to aconductor application of the server as part of a heartbeat message. 18.The computer-implemented method of claim 16, wherein the log data issent to the server once a predetermined amount of log data has beencollected, after a predetermined time period has elapsed, or both. 19.The computer-implemented method of claim 16, wherein the userinteractions comprise which applications the user is using, whatpercentage of time the user is using a given application, which featureswithin the applications the user is using, which features within theapplications that the user is not using, or any combination thereof. 20.The computer-implemented method of claim 16, wherein the userinteractions comprise which features within the applications the user isusing and which features within the applications that the user is notusing.